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Deep learning to frame objects for visual target tracking

dc.contributor.authorPang, Shuchao
dc.contributor.authorCoz Velasco, Juan José del 
dc.contributor.authorYu, Z.
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.contributor.authorDíez Peláez, Jorge 
dc.date.accessioned2018-02-06T10:11:12Z
dc.date.available2018-02-06T10:11:12Z
dc.date.issued2017
dc.identifier.citationEngineering Applications of Artificial Intelligence, 65, p. 406-420 (2017); doi:10.1016/j.engappai.2017.08.010
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/10651/45431
dc.description.sponsorshipThis work was funded by Ministerio de Economía y Competitividad de España (grant TIN2015-65069-C2-2-R), Specialized ResearchFund for the Doctoral Program of Higher Education of China (grant20120061110045) and the Project of Science and Technology Develop-ment Plan of Jilin Province, China (grant 20150204007GX). The work was partially developed while Shuchao Pang was visiting the Universityof Oviedo at Gijón
dc.format.extentp. 406-420
dc.language.isoeng
dc.relation.ispartofEngineering Applications of Artificial Intelligence, 65
dc.rights© 2017 Elsevier Ltd. All rights reserved
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85028774207&doi=10.1016%2fj.engappai.2017.08.010&partnerID=40&md5=cef080f35b58e9cdb274e825db6bffed
dc.titleDeep learning to frame objects for visual target tracking
dc.typejournal article
dc.identifier.doi10.1016/j.engappai.2017.08.010
dc.relation.projectIDMinisterio de Economía y Competitividad/TIN2015-65069-C2-2-R
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.engappai.2017.08.010
dc.rights.accessRightsopen access
dc.type.hasVersionAM


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